class_docstr: Blends the input with a uniform noise input until it is misclassified.
  
  Args:
      distance : Distance measure for which minimal adversarial examples are searched.
      directions : Number of random directions in which the perturbation is searched.
      steps : Number of blending steps between the original image and the random
          directions.
class_docstr_tok: ["Blends","the","input","with","a","uniform","noise","input","until","it","is","misclassified",".","Args",":","distance",":","Distance","measure","for","which","minimal","adversarial","examples","are","searched",".","directions",":","Number","of","random","directions","in","which","the","perturbation","is","searched",".","steps",":","Number","of","blending","steps","between","the","original","image","and","the","random","directions","."]
code: def __init__(
      self,
      *,
      distance: Optional[Distance] = None,
      directions: int = 1000,
      steps: int = 1000,
  ):
      super().__init__(distance=distance)
      self.directions = directions
      self.steps = steps
  
      if directions <= 0:
          raise ValueError("directions must be larger than 0")
  
file_path: foolbox/foolbox/attacks/blended_noise.py
imports: from typing import Union, Optional, Any
  import numpy
  import eagerpy
  from ..devutils import atleast_kd
  from ..distances import Distance
  from .base import FlexibleDistanceMinimizationAttack
  from .base import Model
  from .base import Criterion
  from .base import T
  from .base import get_is_adversarial
  from .base import get_criterion
  from .base import raise_if_kwargs
  import warnings
  from .base import verify_input_bounds
label_desc: Use image node_id 1 to create a new LinearSearchBlendedUniformNoiseAttack object from inherited base classes: FlexibleDistanceMinimizationAttack with example: obj = LinearSearchBlendedUniformNoiseAttack()